Image Classification And Information Retrieval Over Wireless Digital Networks And The Internet

ABSTRACT

A method and system for matching an unknown facial image of an individual with an image of a celebrity using facial recognition techniques and human perception is disclosed herein. The invention provides a internet hosted system to find, compare, contrast and identify similar characteristics among two or more individuals using a digital camera, cellular telephone camera, wireless device for the purpose of returning information regarding similar faces to the user The system features classification of unknown facial images from a variety of internet accessible sources, including mobile phones, wireless camera-enabled devices, images obtained from digital cameras or scanners that are uploaded from PCs, third-party applications and databases. Once classified, the matching person&#39;s name, image and associated meta-data is sent back to the user.

CROSS REFERENCE TO RELATED APPLICATION

The Present Application claims priority to U.S. Provisional PatentApplication No. 60/721226, filed Sep. 28, 2005.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a method and system for classificationof digital facial images received over wireless digital networks or theInternet and retrieval of information associated with classified image.

2. Description of the Related Art

Classification of facial images using feature recognition software iscurrently used by various government agencies such as the Department ofHomeland Security (DHS) and the Department of Motor Vehicles (DMV) fordetecting terrorists, detecting suspected cases of identity fraud,automating border and passport control, and correcting mistakes in theirrespective facial image databases. Facial images stored in the DMV orDHS are digitized and stored in centralized databases, along withassociated information on the person. Examples of companies that providebiometric facial recognition software include Cross Match Technologies,Cognitec, Cogent Systems, and Iridian Technologies; of these, Cognitecalso provides a kiosk for digitally capturing images of people forstorage into their software.

Your face is an important part of who you are and how people identifyyou. Imagine how hard it would be to recognize an individual if allfaces looked the same. Except in the case of identical twins, the faceis arguably a person's most unique physical characteristic. While humanshave had the innate ability to recognize and distinguish different facesfor millions of years, computers are just now catching up.

Visionics, a company based in New Jersey, is one of many developers offacial recognition technology. The twist to its particular software,FACEIT, is that it can pick someone's face out of a crowd, extract thatface from the rest of the scene and compare it to a database full ofstored images. In order for this software to work, it has to know what abasic face looks like. Facial recognition software is based on theability to first recognize faces, which is a technological feat initself, and then measure the various features of each face.

If you look in the mirror, you can see that your face has certaindistinguishable landmarks. These are the peaks and valleys that make upthe different facial features. Visionics defines these landmarks asnodal points. There are about 80 nodal points on a human face. A few ofthe nodal points that are measured by the FACEIT software: distancebetween eyes; width of nose; depth of eye sockets; cheekbones; Jaw line;and chin. These nodal points are measured to create a numerical codethat represents the face in a database. This code is referred to as afaceprint and only fourteen to twenty-two nodal points are necessary forthe FACEIT software to complete the recognition process.

Facial recognition methods may vary, but they generally involve a seriesof steps that serve to capture, analyze and compare your face to adatabase of stored images. The basic process that is used by the FACEITsoftware to capture and compare images is set forth below and involvesDetection, Alignment, Normalization, Representation, and Matching. Toidentify someone, facial recognition software compares newly capturedimages to databases of stored images to see if that person is in thedatabase.

Detection is when the system is attached to a video surveillance system,the recognition software searches the field of view of a video camerafor faces. If there is a face in the view, it is detected within afraction of a second. A multi-scale algorithm is used to search forfaces in low resolution. The system switches to a high-resolution searchonly after a head-like shape is detected.

Alignment is when a face is detected, the system determines the head'sposition, size and pose. A face needs to be turned at least thirty-fivedegrees toward the camera for the system to register the face.

Normalization is when the image of the head is scaled and rotated sothat the head can be registered and mapped into an appropriate size andpose. Normalization is performed regardless of the head's location anddistance from the camera. Light does not impact the normalizationprocess.

Representation is when the system translates the facial data into aunique code. This coding process allows for easier comparison of thenewly acquired facial data to stored facial data.

Matching is when the newly acquired facial data is compared to thestored data and linked to at least one stored facial representation.

The heart of the FACEIT facial recognition system is the Local FeatureAnalysis (LFA) algorithm. This is the mathematical technique the systemuses to encode faces. The system maps the face and creates thefaceprint. Once the system has stored a faceprint, it can compare it tothe thousands or millions of faceprints stored in a database. Eachfaceprint is stored as an 84-byte file.

One of the first patents related to facial recognition technology isRothfjell, U.S. Pat. No. 3,805,238 for a Method For IdentifyingIndividuals using Selected Characteristics Body Curves. Rothfjellteaches an identification system in which major features (e.g. the shapeof a person's nose in profile) are extracted from an image and stored.The stored features are subsequently retrieved and overlaid on a currentimage of the person to verify identity.

Another early facial recognition patent is Himmel, U.S. Pat. No.4,020,463 for an Apparatus And A Method For Storage And Retrieval OfImage Patterns. Himmel discloses digitizing a scanned image into binarydata which is then compressed and then a sequence of coordinates andvector values are generated which describe the skeletonized image. Thecoordinates and vector values allow for compact storage of the image andfacilitate regeneration of the image.

Yet another is Gotanda, U.S. Pat. No. 4,712,103 for a Door Lock ControlSystem. Gotanda teaches, inter alia, storing a digitized facial image ina non-volatile ROM on a key, and retrieving that image for comparisonwith a current image of the person at the time he/she request access toa secured area. Gotanda describes the use of image compression, by asmuch as a factor of four, to reduce the amount of data storage capacityneeded by the ROM that is located on the key.

Yet another is Lu, U.S. Pat. No. 4,858,000. Lu teaches an imagerecognition system and method for identifying ones of a predeterminedset of individuals, each of whom has a digital representation of his orher face stored in a defined memory space.

Yet another is Tal, U.S. Pat. No. 4,975,969. Tal teaches an imagerecognition system and method in which ratios of facial parameters(which Tal defines a distances between definable points on facialfeatures such as a nose, mouth, eyebrow etc.) are measured from a facialimage and are used to characterize the individual. Tal, like Lu in U.S.Pat. No. 4,858,000, uses a binary image to find facial features.

Yet another is Lu, U.S. Pat. No. 5,031,228. Lu teaches an imagerecognition system and method for identifying ones of a predeterminedset of individuals, each of whom has a digital representation of his orher face stored in a defined memory space. Face identification data foreach of the predetermined individuals are also stored in a UniversalFace Model block that includes all the individual pattern images or facesignatures stored within the individual face library.

Still another is Burt, U.S. Pat. No. 5,053,603. Burt teaches an imagerecognition system using differences in facial features to distinguishone individual from another. Burt's system uniquely identifiesindividuals whose facial images and selected facial feature images havebeen learned by the system. Burt's system also “generically recognizes”humans and thus distinguishes between unknown humans and non-humanobjects by using a generic body shape template.

Still another is Turk et al., U.S. Pat. No. 5,164,992. Turk teaches theuse of an Eigenface methodology for recognizing and identifying membersof a television viewing audience. The Turk system is designed to observea group of people and identify each of the persons in the group toenable demographics to be incorporated in television ratingsdeterminations.

Still another is Deban et al., U.S. Pat. No. 5,386,103. Deban teachesthe use of an Eigenface methodology for encoding a reference face andstoring said reference face on a card or the like, then retrieving saidreference face and reconstructing it or automatically verifying it bycomparing it to a second face acquired at the point of verification.Deban teaches the use of this system in providing security for AutomaticTeller Machine (ATM) transactions, check cashing, credit card securityand secure facility access.

Yet another is Lu et al., U.S. Pat. No. 5,432,864. Lu teaches the use ofan Eigenface methodology for encoding a human facial image and storingit on an “escort memory” for later retrieval or automatic verification.Lu teaches a method and apparatus for employing human facial imageverification for financial transactions.

Technologies provided by wireless carriers and cellular phonemanufacturers enable the transmission of facial or object images betweenphones using Multimedia Messaging Services (MMS) as well as to theInternet over Email (Simple Mail Transfer Protocol, SMTP) and WirelessAccess Protocol (WAP). Examples of digital wireless devices capable ofcapturing and receiving images and text are camera phones provided byNokia, Motorola, LG, Ericsson, and others. Such phones are capable ofhandling images as JPEGs over MMS, Email, and WAP across many of thewireless carriers: Cingular, T-Mobile, (GSM/GPRS), and Verizon (CDMA)and others.

Neven, U.S. Patent Publication 2005/0185060, for an Image Base Inquirysystem For Search Engines For Mobile Telephones With Integrated Camera,discloses a system using a mobile telephone digital camera to send animage to a server that converts the image into symbolic information,such as plain text, and furnishes the user links associated with theimage which are provided by search engines.

Neven, et al., U.S. Patent Publication 2006/0012677, for an Image-BasedSearch Engine For Mobile Phones With Camera, discloses a system thattransmits an image of an object to a remote server which generates threeconfidence values and then only generates a recognition output from thethree confidence values, with nothing more. I

Adam et al., U.S. Patent Publication 2006/0050933, for a Single ImageBased Multi-Biometric System And Method which integrates face, skin andiris recognition to provide a biometric system.

The general public has a fascination with celebrities and many membersof the general public use celebrities as a standard for judging someaspect of their life. Many psychiatrists and psychologists believe theconfluence of forces coming together in technology and media have led tothis celebrity worship factor in our society. One output of thiscelebrity factor has been a universal approach to compare or determinethat someone looks like a certain celebrity. People are constantlystating that someone they meet or know looks like a celebrity, whetherit is true or not. What would be helpful would be to scientificallyprovide a basis for someone to lay claim as looking like a certaincelebrity.

BRIEF SUMMARY OF THE INVENTION

The present invention provides a novel method and system for providingthe general public an expedient, inexpensive and technologically easymeans for determining which celebrity someone looks like.

The invention classifies a person, or whom a person most looks like, bypreferably using a digital image captured by a wireless communicationdevice (preferably a mobile telephone) or from a personal computer (PC).The image may be in a JPEG, TIFF, GIF or other standard image format.Further, an analog image may be utilized if digitized. An example iswhich celebrity most resembles the image that was sent to theapplication and can be viewed by the user either through their wirelesscommunication device or through a website. The image is sent to thewireless carrier and subsequently sent over the internet to an imageclassification server. Alternatively, the digital image may be uploadedto a PC from a digital camera or scanner and then sent to the imageclassification server over the internet.

After an image is received by the image classification server, the imageis processed into a feature vector, which reduces the complexity of thedigital image data into a small set of variables that represent thefeatures of the image that are of interest for classification purposes.

The feature vector is compared against existing feature vectors in animage database to find the closest match. The image database preferablycontains one or more feature vectors for each target individual.

Once classified, an image of the best matching person, possiblymanipulated to emphasize matching characteristics, as well as meta-dataassociated with the person, sponsored information, similar product,inventory or advertisement is sent back to the user's PC or wirelesscommunication device.

A more detailed explanation of a preferred method of the invention is asfollows below. The user captures a digital image with a digital cameraenabled wireless communication device, such as a mobile telephone. Thecompressed digital image is sent to the wireless carrier as a multimediamessage (MMS), a short message service (“SMS”), an e-mail (Simple MailTransfer Protocol (“SMTP”)), or wireless application protocol (“WAP”)upload. The image is subsequently sent over the internet using HTTP ore-mail to an image classification server. Alternatively, the digitalimage may be uploaded to a PC from a digital camera, or scanner. Once onthe PC, the image can be transferred over the internet to the imageclassification server as an e-mail attachment, or HTTP upload. The useris the provider of the digital image for classification, and includes,but is not limited to a physical person, machine, or softwareapplication.

After the image is received by the image classification server, afeature vector is generated for the image. A feature vector is a smallset of variables that represent the features of the image that are ofinterest for classification purposes. Creation and comparison offeatures vectors may be queued, and scaled across multiple machines.Alternatively, different feature vectors may be generated for the sameimage. Alternatively, the feature vectors of several images of the sameindividual may be combined into a single feature vector. The incomingimage, as well as associate features vectors, may be stored for laterprocessing, or added to the image database. For faces, possible featurevector variables are the distance between the eyes, the distance betweenthe center of the eyes, to the chin, the size, and shape of theeyebrows, the hair color, eye color, facial hair if any, and the like.

After the feature vector for an image is created, the feature vector iscompared against feature vectors in an image database to find theclosest match. Preferably, each image in the image database has afeature vector. Alternatively, feature vectors for the image databaseare created from a set of faces, typically eight or more digital imagesat slightly different angles for each individual. Since the targetindividual's feature vector may be generated from several images, anoptional second pass is made to find which of the individual images thatwere used to create the feature vector for the object best match theincoming image.

Once classified, the matching image's name and associated meta-data isretrieved from the database. Before the response is sent, thebest-matching image or incoming image may be further manipulated toemphasize the similarities between the two images. This imagemanipulation can be automated, or can be done interactively by the user.The matching image's name, meta-data, associated image, and a copy ofthe incoming image are then sent back to the user's wirelesscommunication device or PC, and also to a web page for the user.

One preferred aspect of the present invention is a method for matchingimages. The method includes acquiring a facial image of a human. Next,the facial image is transmitted from a sender to a server. Next, thefacial image is analyzed at the server to determine if the facial imageis acceptable. Next, the facial image is processed to create a processedimage. Next, the processed image is compared to a plurality of databaseprocessed images. Next, the processed image is matched to a databaseprocessed image of the plurality of database processed images to creatematched images. Next, a perception value of the matched images isdetermined at the server site. Then, the matched images and theperception value are transmitted to the sender.

Another aspect of the present invention is a method for matching anunknown image to an image of a celebrity. The method includes wirelesslytransmitting an unknown digital facial image of an individual from amobile communication device over a wireless network to an imageclassification server. Next, the digital facial image is processed atthe image classification server to create a primary feature vector forthe digital facial image. Next, the primary feature vector is comparedto a plurality of database feature vectors, with each of the pluralityof database feature vectors corresponding to a database processed image.Next, a database feature vector is selected that best matches theprimary feature vector to create matched images of the unknown digitalfacial image of the individual and a celebrity. Next, the matched imagesare transmitted to the mobile communication device.

Yet another aspect of the present invention is a system for matching anunknown facial image of an individual with an image of a celebrity. Thesystem includes a mobile communication device, an image classificationserver and a wireless network. The mobile communication device includesmeans for generating a digital facial image of an individual and meansfor wireless transmitting the digital facial image. The imageclassification server has means for receiving the digital facial imagefrom the mobile communication device, means for analyzing the digitalfacial image, means for processing the digital facial image to generatea processed image, means for comparing the processed image to aplurality of database processed images, means for matching the processedimage to a database processed image of the plurality of databaseprocessed images to create matched images, means for determining aperception value of the matched images, and means for transmitting thematched images and the confidence value to the mobile communicationdevice. The wireless network allows for transmissions between the mobilecommunication device and the image classification server.

One object is to provide a system using a digitally stored image tofind, compare, contrast and identify similar characteristics among twoor more individuals. The image can be produced by a digital camera, ordigitally scanned from an original, analog image.

Another object is that the system uses the transfer of the image to anapplication and database accessed via the internet, TCP/IP, WAP, MMS,SMS, or SMTP.

Yet another object is that the internet accessible application iscompleted via a connection to the internet by a multitude of methods,including but not limited to web browser, WAP Browser, MMS, SMS, andSMTP.

Yet another object is that the image is processed to identify usingoff-the shelf feature vector recognition software (or as may bedeveloped in the future) and compared with a database of one or more, ora plurality of feature vectors. The database of feature vectors isgenerated from other images or sources.

Yet another object is that the results of the image comparisons are thendisplayed to the user by accessing the internet through a web browser,WAP browser, or pushed down to the user using MMS, SMS, and SMTP.

Yet another object is that the browser accessible original image and/orthe resulting image matches or comparisons can be viewed by the userusing either an internet connected browser, a wireless communicationdevice or through a terminal.

Yet another object is that the application can compare or contrast anyplurality of available images. The user may chose the database of imagesto compare including those made available by the host, created by theuser or supplied by third parties.

Yet another object is that the resulting information provided to theuser may include third party information, advertisements, banners,pop-ups, or click-through.

Yet another object is that the system can determine the closest matchfor the user's submitted digital facial image against a database ofcelebrities, including, but not limited to actors, actresses, musicians,athletes, models, and government officials.

Having briefly described the present invention, the above and furtherobjects, features and advantages thereof will be recognized by thoseskilled in the pertinent art from the following detailed description ofthe invention when taken in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a flow chart of a specific method of the present invention.

FIG. 2 is a flow chart of a general method of the present invention.

FIG. 3 is a schematic diagram of a system of the present invention.

FIG. 3A is a schematic representation of the image classification serverof the present invention.

FIG. 4 is image and table comparison of an unknown image and a celebrityimage.

DETAILED DESCRIPTION OF THE INVENTION

A flow chart of a preferred specific method of the present invention isillustrated in FIG. 1. The method is generally designated 100 andcommences with a facial image of individual being acquired at block 101.The facial image is acquired preferably using a digital camera of awireless communication device such as a wireless mobile telephone,personal digital assistant (“PDA”) or the like. Alternatively, thefacial image is acquired from a PC or the like.

At block 102, the facial image is transmitted over a network to an imageclassification server, preferably over a wireless network. The facialimage is preferably sent to a male or female designation site at theimage classification server. The facial image is subsequently sent overthe internet using HTTP or e-mail to the image classification server.The facial image, preferably a compressed digital facial image such as aJPEG image, is sent to a wireless carrier as a MMS, a SMS, a SMTP, orWAP upload. Alternatively, the facial image is uploaded to a PC from adigital camera, or scanner and then transferred over the internet to theimage classification server as an e-mail attachment, or HTTP upload.

At block 103, the facial image is analyzed at the image classificationsserver to determine if the facial image is of adequate quality to beprocessed for matching. Quality issues with the facial image include butare not limited to a poor pose angle, brightness, shading, eyes closed,sunglasses worn, obscured facial features, or the like. At block 104, animage determination is made concerning the quality of the image. Anegative image determination is made at block 105. At block 106, atransmission is sent to the sender informing then sender that the facialimage provided is inadequate and requesting that the sender provide anew facial image. The matching procedure for such a negative image maycontinue, and the matched images will be sent with an additionalstatement informing the sender that the image was of bad quality andthat a better match may be possible with a higher quality image.

At block 107, if the facial image is positive, then the facial image isprocessed at block 108. It should be noted that the facial image ispreviously unknown to the image classification and is the first timethat the facial image has been analyzed by the image classificationserver. Thus, the method of present invention involves processing anunknown image to find a match with facial images of other individuals,which is unlike typical facial recognition systems which involvematching an image of an individual with a known image of the individualin the database. At block 108, processing of image preferably comprisesusing an algorithm which includes a principle component analysistechnique to process the face of the facial image into an average of amultitude of faces, otherwise known as the principle component and a setof images that are the variance from the average face image known as theadditional components. Each is reconstructed by multiplying theprincipal components and the additional components against a featurevector and adding the resulting images together. The resulting imagereconstructs the original face of the facial image. Processing of thefacial image comprises factors such as facial hair, hair style, facialexpression, the presence of accessories such as sunglasses, hair color,eye color, and the like. Essentially a primary feature vector is createdfor the facial image.

At block 109, processed image or primary feature vector is compared to aplurality of database processed images preferably located at the imageclassification server. During the comparison, the primary feature vectoris compared a plurality of database feature vectors which represent theplurality of database processed images. The database preferably includesat least 10,000 processed images, more preferably at least 50,000processed images, and most preferably from 50,000 processed images to100,000 processed images. Those skilled in the pertinent art willrecognize that the database may contain any number of images withoutdeparting from the scope ans spirit of the present invention. Theprocessed images preferably include multiple images of one individual,typically from two to twenty images, more preferably from four to tenimages of a single individual in different poses, different facialexpressions, different hair styles and the like. The database ofprocessed images preferably includes celebrities, including, but notlimited to actors, actresses, musicians, athletes, models, governmentofficials, and other publicly well-known individuals. Again, it shouldbe noted that the facial image sent by the sender is an unknown imagewhich is being best matched to a known image.

At block 110, the processed image undergoes raw matching of a smallplurality of database images with each having a feature vector valuethat is close to the value of the primary feature vector. At block 110a, the iterative processing of the raw matching is performed wherein thehuman perception of what is a good match is one of the primary factorsin creating the matched images. At block 111, a perception value for thematched images is determined based on the feature vector values. Theperception value ranges from 0% to 100%, with 100% being an ideal match.

At block 112, the matched images and the perception value aretransmitted to the sender over a network as discussed above for theinitial transmission. The entire process preferably occurs within a timeperiod of sixty seconds, and most preferably within a time of tenseconds. The process may be delayed due to the wireless carrier, andnetwork carrier. In this manner, the sender will know which celebritythe facial image best matches. The output of the matched images and anyadditional text is preferably sent to the sender's wirelesscommunication device for instantaneous feedback of their inquiry ofwhich celebrity does the facial image look like. Further, the output isalso sent to a sender's web page on a web site hosted through the imageclassification server wherein the sender can control access to thesender's web page and modify the matched images and the additional text.Further, the output is sent to a voting site as discussed below.

At decision 113, the quality of the matched images is determined todecide if the matched images should be sent to voting site on the website. At block 115, the matched images are sent to the sender's wirelesscommunication device, the sender's web page on the web site for viewingby the sender and other viewers determined by the sender. At block 114,the matched images are sent to the voting site if of sufficient quality,preferably based on the perception value, to be voted upon by visitorsto the voting site.

In this manner, a statistical modeling element is added to the matchingprocess to better match images based on human perception as determinedby the scores for previously matched images on the voting site. In otherembodiments regression analysis or Bayesian analysis is utilized. Underthis alternative scenario, a Support Vector Machine, preferably ahigh-dimensional neural network, with two feature vectors of a match,along with average vote scores collected from viewers of the web sitewill be utilized to provide better matching of images. A more detailedexplanation of a Support Vector Machine is set forth in Cortes & Vapnik,Support Vector Networks, Machine Learning, 20, 1995, which is herebyincorporated by reference in its entirety. The previous voting patternsare implemented in a statistical model for the algorithm to capture thehuman perception element to better match images as perceived by humans.

A more general method of the present invention is illustrated in FIG. 2.The general method is designated 150. At block 151, an unknown imagefrom a wireless communication device such as a mobile telephone istransmitted from a sender to an image classification server over anetwork such as a wireless network with subsequent internettransmission. At block 152, the unknown image is processed to create aprimary feature vector such as discussed above. At block 153, theprimary feature vector value is compared to a plurality of databasefeature vectors. At block 154, a database feature vector that bestmatches the primary feature vector is selected to create matched images.At block 155, the matched images are transmitted to the sender, alongwith a confidence value and other information about the matching image.

A system of the present invention is illustrated in FIG. 3. The systemis generally designated 50. The system 50 preferably comprises awireless communication device 51, a wireless network 52, an imageclassification server 53 and a web site 55, not shown, which may beviewed on a computer 54 or alternate wireless communication device 54′with internet access. The wireless communication device preferablycomprises means for generating a digital facial image of an individualand means for wirelessly transmitting the digital facial image over awireless network. The image classification server 53 preferablycomprises means for analyzing the digital facial image, means forprocessing the digital facial image to generate a processed image, meansfor comparing the processed image to a plurality of database processedimages, means for matching the processed image to a database processedimage to create matched images, means for determining a perceptionvalue, means for applying a statistical model based on human perceptionas determined by user's votes of previous third party matched images,and means for transmitting the matched images and the perception valueto the wireless communication device.

The present invention preferably uses facial recognition softwarecommercially or publicly available such as the FACEIT brand softwarefrom IDENTIX, the FACEVACS brand software from COGNETIC, and others.Those skilled in the pertinent art will recognize that there are manyfacial recognition softwares, including those in the public domain, thatmay be used without departing from the scope and spirit of the presentinvention.

The operational components of the image classification server 53 areschematically shown in FIG. 3A. The image classification server 53preferably comprises an input module 62, transmission engine 63, inputfeed 64, feature vector database 65, sent images database 66, facialrecognition software 67, perception engine 68, output module 69 and thecelebrity image database 70. The input module 62 is further partitionedinto wireless device inputs 62 a, e-mail inputs 62 b and HTTP (internet)inputs 62 c. The output module 69 is further partitioned into wirelessdevice outputs 69 a, a sender's web page output 69 b and a voting webpage output 69 c. The feature vector database 65 is the database ofprocessed images of the celebrities from which the previously unknownfacial image is matched with one of the processed images. The celebrityimage database is a database of the actual images of celebrities whichare sent as outputs for the matched images. Such image databases arecommercially available from sources such as Photorazzi. The sent imagesdatabase 66 is a database of all of the images sent in fromusers/senders to be matched with the processed images. The perceptionengine 68 imparts the human perception processing to the matchingprocedure.

As shown in FIG. 4, an unknown facial image 80 sent by an individual ismatched to a celebrity image 75 selected from the database of processedimages using a method of the present invention as set forth above. Thetable provides a comparison of the facial values for each of the images.

From the foregoing it is believed that those skilled in the pertinentart will recognize the meritorious advancement of this invention andwill readily understand that while the present invention has beendescribed in association with a preferred embodiment thereof, and otherembodiments illustrated in the accompanying drawings, numerous changesmodification and substitutions of equivalents may be made thereinwithout departing from the spirit and scope of this invention which isintended to be unlimited by the foregoing except as may appear in thefollowing appended claim. Therefore, the embodiments of the invention inwhich an exclusive property or privilege is claimed are defined in thefollowing appended claims.

1. A method for matching an unknown image of an individual with a knownimage of another individual, the method comprising: acquiring an unknownfacial image of an individual; transmitting the unknown facial imagefrom a sender over a network to a server; analyzing the facial image atthe server to determine if the unknown facial image is acceptable;processing the unknown facial image to create a processed image;comparing the processed image to a plurality of database processedimages; matching the processed image to a database processed image ofthe plurality of database processed images to create matched images,wherein the database processed image is a facial image of anotherindividual; determining a perception value of the matched images; andtransmitting the matched images and the perception value to the senderover the network.
 2. The method according to claim 1 wherein theprocessed image is processed as a primary feature vector and theplurality of database processed images is a plurality of databasefeature vectors, and wherein comparing the processed image to aplurality of database processed images comprises comparing the primaryfeature vector to each of the plurality of database feature vectors. 3.The method according to claim 2 wherein matching the processed image toa database processed image of the plurality of database processed imagesto create matched images comprises selecting a database feature vectorwith a value that is most similar to the value of the primary featurevector.
 4. The method according to claim 1 wherein the plurality ofdatabase processed images comprises at least 10,000 database processedimages.
 5. The method according to claim 2 wherein comparing theprocessed image to a plurality of database processed images furthercomprises applying a statistical model based on human perception asdetermined by user's votes of previous third party matched images. 6.The method according to claim 1 wherein the plurality of databaseprocessed images comprises from 50,000 database processed images to100,000 database processed images.
 7. The method according to claim 1wherein the method occurs within a time period of 60 seconds.
 8. Themethod according to claim 1 further comprising filtering the databaseprocessed image to provide a preferred database processed image for thematched images.
 9. The method according to claim 2 wherein the primaryfeature vector and each of the plurality of database feature vectors arebased on a plurality of factors comprising facial expression, hairstyle, hair color, facial pose, eye color, texture of the face, color ofthe face and facial hair.
 10. The method according to claim 1 whereintransmitting the unknown facial image from a sender to a servercomprises transmitting the unknown facial image to a male server site ora female server site.
 11. The method according to claim 1 wherein theunknown facial image is acquired by a digital camera and transmittedfrom a computer over the internet to the server, or the unknown facialimage is acquired by a camera of a mobile telephone and transmitted fromthe mobile telephone over a wireless network to the server.
 12. Themethod according to claim 1 wherein the unknown facial image is a JPEGimage transmitted as a MMS.
 13. The method according to claim 12 whereinthe matched images and perception value is transmitted from the serverover a wireless network to the mobile telephone.
 14. The methodaccording to claim 1 wherein the matched images and perception value aretransmitted to a sender's web page on a web site.
 15. The methodaccording to claim 1 wherein the perception value ranges from 0% to100%.
 16. The method according to claim 1 wherein analyzing the unknownfacial image at the server comprises determining if a plurality offacial image factors are acceptable, the plurality of facial imagefactors comprising the lack of a facial image, the lack of eyes, unevenlighting, the brightness of the facial image, pose angle of the facialimage, relative size of the facial image and pixel strength of thefacial image.
 17. The method according to claim 1 further comprisingtransmitting a biography summary of a celebrity of the matched imagefrom the server to the sender.
 18. A method for matching images, themethod comprising: wirelessly transmitting a digital facial image from amobile communication device over a wireless network to an imageclassification server; processing the digital facial image at the imageclassification server to create a primary feature vector for the digitalfacial image; comparing the primary feature vector to a plurality ofdatabase feature vectors, each of the plurality of database featurevectors corresponding to a database processed image; selecting adatabase feature vector that best matches the primary feature vector tocreate matched images; and transmitting the matched images to the mobilecommunication device.
 19. A system for matching an unknown facial imageof an individual with an image of a celebrity, the system comprising: amobile communication device comprising means for generating a digitalfacial image of an individual and means for wireless transmitting thedigital facial image; an image classification server for receiving thedigital facial image from the mobile communication device, the imageclassification server comprising means for analyzing the digital facialimage, means for processing the digital facial image to generate aprocessed image, means for comparing the processed image to a pluralityof database processed images, means for matching the processed image toa database processed image of the plurality of database processed imagesto create matched images, means for determining a perception value ofthe matched images, and means for transmitting the matched images andthe perception value to the mobile communication device; and a wirelessnetwork for transmissions between the mobile communication device andthe image classification server.
 20. The system according to claim 19wherein the image classification server further comprises means forapplying a statistical model based on human perception as determined byuser's votes of previous third party matched images.